The manner in which a measured process variable responds over time to changes in the controller output signal is fundamental to the design and tuning of a PID controller. The best way to learn about the dynamic behavior of a process is to perform experiments, commonly referred to as “bump tests.” Critical to success is that the process data generated by the bump test be descriptive of actual process behavior.
Model-Based Tuning Methods for PID Controllers discusses the qualities required for the “good” dynamic data and methods for modeling the dynamic data for controller design. Parameters from the dynamic model are not only used in correlations to compute tuning values but also provide insight into controller design parameters such as loop sample time and whether dead time presents a performance challenge. It is becoming increasingly common for dynamic studies to be performed with the controller in automatic (closed loop). For closed-loop studies, the dynamic data is generated by bumping the set point. The method for using closed data, along with all illustrated concepts in this white paper use a level control simulation.
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